ISSN : 1796-203X
Volume : 4    Issue : 8    Date : August 2009

Texture Image Classification Using Visual Perceptual Texture Features and Gabor Wavelet Features
Muwei Jian, Haoyan Guo, and Lei Liu
Page(s): 763-770
Full Text:
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Texture can describe a wide variety of surface characteristics and a key component for human visual
perception and plays an important role in image-related applications. This paper proposes a scheme
for texture image classification using visual perceptual texture features and Gabor wavelet features.
Three new texture features which are proved to be in accordance with human visual perceptions are
introduced. Usually, Subband statistics based on Gabor wavelet features are normally used to
construct feature vectors for texture image classification. However, most previous methods make no
further analysis of the decomposed subbands or simply remove most detail coefficients. The
classification algorithms commonly use many features without consideration of whether the features
are effective for discriminating different classes. This may produce unnecessary computation burden
and even decrease the retrieval performance. This paper proposes a method for selecting effective
Gabor wavelet subbands based on feature selection functions. The method can discard those
subbands that are redundant or may lead to wrong classification results. We test our proposed method
using the Brodatz texture database, and the experimental results show the scheme has produced
promising results.

Index Terms
Visual Perception Texture Features, Gabor wavelet features; Texture image classification; SVM